Apache Airflow

Airflow orchestrates complex data pipelines by letting you define workflows as code. Instead of scheduling scripts with cron and hoping they run in order, you write Python that describes dependencies between tasks. The scheduler handles retries, parallelism, and backfills automatically. A web interface shows what's running, what failed, and where data is in your pipeline. Alerts notify you when things go wrong. Data engineering teams treat Airflow as essential infrastructure. When pipelines grow from a few scripts to hundreds of interconnected jobs, Airflow provides the structure that keeps everything manageable.